Preface

Statistical inference has multiple applications in the field of computer vision, which dealt with interdisciplinary topics ranging from biomedical imaging, object recognition, motion tracking and so on. Intuitively, the geometry of such visual data can be one of the most important feature for solving such problems. Since the optimization of computational expenses can determine the pragmatic of implementation, the question we can ask is that how can we map the input space to lower dimensional space while preserving as much local feature as possible. This leads to the development of statistical learning on geometry and topology in order to tackle the challenge. This capstone project tries to provide an intuitive introduction with examples included in.